Abstract
Can machine learning algorithms be trained to recognise beauty in music?
To what extent is human recognition of beauty in music cultural, or crosscultural?
Music is prevalent in all human cultures. Music ...

Motivation: Mutant phenotype growth experiments are an important novel source of functional genomics data which have received little attention in bioinformatics. We applied supervised machine learning to the problem of ...

ABSTRACT
Breast cancer is currently the most common cancer among women worldwide.
Mammography has been the most reliable and e ective screening tool for the
early detection of breast cancer. Recently, computer-aided ...

Mammographic risk assessment is becoming increasingly important in decision making in screening mammography and computer aided diagnosis systems. Strong evidence shows that characteristic patterns of breast tissue as seen ...

Mammographic risk assessment is concerned with the probability of a woman developing breast cancer. Recently, it has been suggested that the density of linear structures is related to risk. Two independent sets of mammographic ...

We present an approach to automate texton selection to achieve optimized mammogram segmentation results with respect to mammographic building blocks (i.e. nodular, linear, homogeneous, and radiolucent) as described by ...

OBJECTIVE: To assess the impact of the introduction of an ultrasound-based model of care for women with acute gynecological complications. METHODS: This was a prospective comparative study of women attending an ultrasound-based ...

Over the past three decades, tactile sensing has developed into a sophisticated technology. There has been a longstanding and widely held expectation that tactile sensors would have a major impact on industrial robotics ...

For supervised learning, feature selection algorithms attempt to maximise a given function of predictive accuracy. This function usually considers the ability of feature vectors to reflect decision class labels. It is ...

Abstract. This paper describes an artiﬁcial immune system algorithm which implements a fairly close analogue of the memory mechanism proposed by Jerne(1) (usually known as the Immune Network Theory). The algorithm demonstrates ...

In 2005, the Metabolomics Standards Initiative has been formed. An outline and general introduction is provided to inform about the history, structure, working plan and intentions of this initiative. Comments on any of the ...

Bayesian networks have become a standard technique in the representation of uncertain knowledge. This paper proposes methods that can accelerate the learning of a Bayesian network structure from a data set. These methods ...

The performance of Evolutionary Programming (EP) is affected by many factors (e.g. mutation operators and selection strategies). Although the conventional approach with Gaussian mutation operator may be efficient, the ...

Multigrid methods have been proven to be an efficient approach in accelerating the convergence rate of numerical algorithms for solving partial differential equations. This paper investigates whether multigrid methods are ...

The expressive power, powerful search capability, and the explicit nature of the resulting models make evolutionary methods very attractive for supervised learning applications in bioinformatics. However, their characteristics ...

Computer-aided mammographic prompting systems require the reliable detection of a variety of signs of cancer. In this paper we concentrate on the detection of spiculated lesions in mammograms. A spiculated lesion is typically ...

Failure Mode and Effects Analysis is widely used in engineering hardware systems to help in understanding the effects of potential failures and the faults that cause them to occur. The analysis is iterative leading to ...